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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series Data

IEEE Transactions on Services Computing 2025年

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Radha Mahendran Ramanakar Reddy Danda Asma Anjum Rashmi Abhay Kamde Bhagyashree Shailesh Firake Jayshree Suhas Wasnik Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Chennai India Architect Hyderabad Telangana India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Electrical Engineering K J Somaiya Polytechnic Mumbai Maharashtra India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide.

关键词： Precision agriculture Technological innovation Adaptation models Accuracy Visual analytics Crops Artificial neural networks Genetics Sustainable development Optimization

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Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification

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IEEE Transactions on Biomedical engineering 2017年第9期64卷 2098-2109页

作者： Wang, Lei Pedersen, Peder C. Agu, Emmanuel Strong, DIane M. Tulu, Bengisu Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute United States Department of Computer Science Worcester Polytechnic Institute United States Foisie School of Business Worcester Polytechnic Institute United States

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: In the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis. © 2017 IEEE.

关键词： Support vector machines

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

引用

2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Mahendran, Radha Danda, Ramanakar Reddy Anjum, Asma Kamde, Rashmi Abhay Firake, Bhagyashree Shailesh Wasnik, Jayshree Suhas Vels Institute of Science Technology and Advanced Studies Department of Bioinformatics Chennai India Architect Telangana Hyderabad India Hkbk College of Engineering Visvesvaraya Technological University Department of Computer Science and Engineering Bangalore India K J Somaiya Polytechnic Department of Electrical Engineering Maharashtra Mumbai India

ISBN: (纸本)9798331521394

关键词： Precision agriculture

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PROPERTIES OF THE WEIERSTRASS FUNCTION IN THE TIME AND FREQUENCY DOMAINS

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CHAOS SOLITONS & FRACTALS 1995年第1期5卷 1-8页

作者： NICOLETTI, D Electrical and Computer Engineering Department Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609 USA

The Weierstrass function is continuous everywhere and differentiable nowhere. It is used advantageously for modeling fractally coarse media. The frequency domain expression is analyzed using the Laplace transform of a truncated Weierstrass function. The poles of this function constitute a geometric sequence on the imaginary axis of the complex frequency plane. computer calculations show that the zeros change little as the order is increased. Theoretical development reveals the nature of the dependence of the zeros on order and the parameters of the function. The effect of the scale of observation on the function's properties is examined in both time and frequency domains.

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NUMERICAL-SOLUTIONS OF TM SCATTERING USING AN OBLIQUELY CARTESIAN FINITE-DIFFERENCE TIME-DOMAIN ALGORITHM

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IEE PROCEEDINGS-H MICROWAVES ANTENNAS AND PROPAGATION 1993年第1期140卷 23-28页

作者： LEE, JF Department of Electrical Engineering Worcester Polytechnic Institute Worcester USA

The conventional finite difference time domain (FDTD) algorithm for solving electromagnetic scattering problems, which uses a uniform Cartesian grid for enmeshing the problem domain, is limited in its ability to model scatterers of arbitrary shapes. In this paper, we extend the FDTD algorithm to a general obliquely Cartesian coordinate system, and apply it in conjunction with an edge-type absorbing boundary condition (ABC) to solve a number of representative TM scattering problems. In addition to extending the FDTD algorithm to obliquely Cartesian grids, we also derive the stability condition for the two-dimensional obliquely Cartesian FDTD algorithm.

关键词： ELECTROMAGNETIC SCATTERING FINITE DIFFERENCE TIME DOMAIN

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DYNAMIC ADDRESS-VECTOR QUANTIZATION OF RGB COLOR IMAGES

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IEE PROCEEDINGS-I COMMUNICATIONS SPEECH AND VISION 1991年第4期138卷 225-231页

作者： FENG, YS NASRABADI, NM Department of Electrical Engineering Worcester Polytechnic Institute Worcester USA

Address-vector quantisation (A-VQ) is a new class of vector quantisation (VQ) technique that exploits the statistical redundancy between the neighbouring blocks, to reduce the bit rate to a much smaller value than a standard vector quantiser can achieve. The underlying idea of A-VQ is that the interblock correlation is exploited by encoding a group of neighbouring VQ addresses together. The VQ addresses are obtained by vector quantisation of each individual block. An address codebook, which consists of a set of address codevectors, is then used to encode the neighbouring VQ addresses together;this process introduces no extra coding error since it is a vector dimensionality reduction process. In the paper, the A-VQ coding technique is extended to encode RGB colour images by utilising the interblock as well as the intercolour correlation. Also, a modified A-VQ technique named dynamic address-vector quantisation (dynamic A-VQ) is introduced for the encoding of colour images. In the dynamic A-VQ, the encoder dynamically tracks the local characteristics of the input image. Experimental results show reconstructed images at bit rates of 0.5-0.6 bits per pixel. The resulting bit rate is less than a standard VQ coding technique by a factor of a at least two, with the same quality of reconstructed images.

关键词： IMAGE PROCESSING

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STATISTICAL MODELING AND computer-SIMULATION OF INDOOR RADIO CHANNEL

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IEE PROCEEDINGS-I COMMUNICATIONS SPEECH AND VISION 1991年第3期138卷 153-161页

作者： GANESH, R PAHLAVAN, K Electrical Engineering Department Worcester Polytechnic Institute Worcester USA

Multipath profiles obtained from radio propagation measurements at 910 MHz are used to analyse the statistical characteristics of the indoor radio channel. The data base is divided into two classes: manufacturing floors and college offices. In the manufacturing floors, there is plenty of open space without any presence of walls, as a result of which most of the received power is concentrated in the initial paths. The college office areas have a wider spread of power in delay, because of less open space and the frequent obstruction of the signal between the transmitter and the receiver by one or more walls. The statistical parameters required for computer simulation of multipath profiles in each environment are determined. The arrival of the paths is shown to fit a modified Poisson process and the amplitude of the paths follow a log-normal distribution. The mean and the standard deviation of the log-normal distribution are shown to decay exponentially with delay. To evaluate the performance of this simulation model, the distribution of the RMS delay spreads from the simulated profiles is compared with that obtained from the measured profiles.

关键词： RADIOCOMMUNICATION SIMULATION PROPAGATION

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DFE: Deep Flow Embedding for Robust Network Traffic Classification

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1597-1612页

作者： Wang, Zhijiong Zhang, Anguo Li, Hung Chun Yin, Yadong Chen, Wei Lam, Chan Tong Mak, Peng Un Vai, Mang I. Gao, Yueming Pun, Sio Hang Zhuhai UM Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory Zhuhai519031 China Lingyange Semiconductor Inc. Zhuhai519030 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics 999078 China Fuzhou University School of Physics and Information Engineering Fuzhou350108 China Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Human Phenome Institute Shanghai200437 China Fudan University Human Phenome Institute Shanghai200437 China Macau Polytechnic University Faculty of Applied Sciences 999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China Fuzhou University Key Lab of Medical Instrumentation and Pharmaceutical Technology of Fujian Province Fuzhou350108 China

People's increasing demand for high-quality network services has prompted the continuous attention and development of network traffic classification (NTC). In recent years, deep flow inspection (DFI) is considered to be the most effective and promising method to solve the NTC. However, DFI still cannot effectively address the problem of changes in flow characteristics of complex packet flows and the discovery of new traffic categories. In this paper, we propose a metric learning based deep learning solution with feature compressor, named deep flow embedding (DFE). The feature compressor is used to compress the feature information transmitted layer by layer in DL backbone while maintaining the computational accuracy, so that the backbone can remove as much noise, redundancy, and other irrelevant information from the input data as possible, and achieve more robust feature extraction of network traffic flow. The deep learning (DL) backbone generates an embedding vector for each network packet flow. Then the embedding vector is compared with the vector template preset for each traffic type in the template library to determine the category of the packet flow. Experimental results verify that our method is more effective than the traditional DFI methods in overcoming the problems of flow characteristics variation and new category discovery. © 2013 IEEE.

关键词： Network embeddings

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THE LAGRANGIAN METHOD FOR SOLVING CONSTRAINED MARKOV GAMES

arXiv

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arXiv 2025年

作者： Das, Soham Paternain, Santiago Chamon, Luiz F.O. Eksin, Ceyhun Department of Industrial and Systems Engineering Texas A&M University College StationTX United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Center for Applied Mathematics Ecole Polytechnique Palaiseau France

We propose the concept of a Lagrangian game to solve constrained Markov games. Such games model scenarios where agents face cost constraints in addition to their individual rewards, that depend on both agent joint actions and the evolving environment state over time. Constrained Markov games form the formal mechanism behind safe multiagent reinforcement learning, providing a structured model for dynamic multiagent interactions in a multitude of settings, such as autonomous teams operating under local energy and time constraints, for example. We develop a primal-dual approach in which agents solve a Lagrangian game associated with the current Lagrange multiplier, simulate cost and reward trajectories over a fixed horizon, and update the multiplier using accrued experience. This update rule generates a new Lagrangian game, initiating the next iteration. Our key result consists in showing that the sequence of solutions to these Lagrangian games yields a nonstationary Nash solution for the original constrained Markov *** Codes 93A16, 91A15 (Primary) 91A26, 49N15 (Secondary) © 2025, CC BY-NC-SA.

关键词： Lagrange multipliers

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